OpenRaiser/Intern-Atlas huggingface dataset:
On the Additivity of Optimal Rates for Independent Zero-Error Source and Channel Problems
Venue: arXiv cs.IT 2025
Year: 2025

Zero-error coding encompasses a variety of source and channel problems where the probability of error must be exactly zero. This condition is stricter than that of the vanishing error regime, where the error probability goes to zero as the code blocklength goes to infinity. In general, zero-error coding is an open combinatorial question. We investigate two unsolved zero-error problems: the source coding problem with side information and the channel coding problem. We focus our attention on families of independent problems for which the probability distribution decomposes into a product of probability distributions. A crucial step is the additivity property of the optimal rate, which does not always hold in the zero-error regime, unlike in the vanishing error regime. When the additivity holds, the concatenation of optimal codes is optimal. We derive a condition under which the additivity of the complementary graph entropy $\overline{H}$ for the AND product of graphs and for the disjoint union of graphs are equivalent. Then we establish the connection with a recent result obtained by Wigderson and Zuiddam and by Schrijver, for the zero-error capacity $C_0$. As a consequence, we provide new single-letter characterizations of $\overline{H}$ and $C_0$, for example when the graph is a product of perfect graphs, which is not perfect in general, and for the class of graphs obtained by the product of a perfect graph $G$ with the pentagon graph $C_5$. By building on Haemers result for $C_0$, we also show that the additivity of $\overline{H}$ does not hold for the product of the Schläfli graph with its complementary graph.
A Refreshed Similarity-based Upsampler for Direct High-Ratio Feature Upsampling
Venue: arXiv cs.CV 2025
Year: 2025

Feature upsampling is a fundamental and indispensable ingredient of almost all current network structures for dense prediction tasks. Recently, a popular similarity-based feature upsampling pipeline has been proposed, which utilizes a high-resolution feature as guidance to help upsample the low-resolution deep feature based on their local similarity. Albeit achieving promising performance, this pipeline has specific limitations: 1) HR query and LR key features are not well aligned; 2) the similarity between query-key features is computed based on the fixed inner product form; 3) neighbor selection is coarsely operated on LR features, resulting in mosaic artifacts. These shortcomings make the existing methods along this pipeline primarily applicable to hierarchical network architectures with iterative features as guidance and they are not readily extended to a broader range of structures, especially for a direct high-ratio upsampling. Against the issues, we meticulously optimize every methodological design. Specifically, we firstly propose an explicitly controllable query-key feature alignment from both semantic-aware and detail-aware perspectives, and then construct a parameterized paired central difference convolution block for flexibly calculating the similarity between the well-aligned query-key features. Besides, we develop a fine-grained neighbor selection strategy on HR features, which is simple yet effective for alleviating mosaic artifacts. Based on these careful designs, we systematically construct a refreshed similarity-based feature upsampling framework named ReSFU. Extensive experiments substantiate that our proposed ReSFU is finely applicable to various types of architectures in a direct high-ratio upsampling manner, and consistently achieves satisfactory performance on different dense prediction applications, showing superior generality and ease of deployment.
What Should Be Considered to Support well-being with AI: Considerations Based on Responsible Research and Innovation
Venue: arXiv cs.HC 2025
Year: 2025

To achieve people's well-being with AI systems, we should enable each user to be guided to a healthier lifestyle in a way that is appropriate for her or him. However, there is a dilemma between general well-being as defined in academic and medical discussions and the autonomy users should have when deciding how to promote their well-being. In this position paper, we discuss the difficulty for AI application developers to fully consider in the design phase what might happen to the user, taking an example of a running application (app). We sort out the required factors to enable AI apps that support well-being to address the dilemma between unilaterally defined well-being and human autonomy based on the four dimensions required for responsible innovation: inclusion, anticipation, reflexivity, and responsiveness.
A Pattern Language for Machine Learning Tasks
Venue: arXiv cs.LG 2025
Year: 2025

We formalise the essential data of objective functions as equality constraints on composites of learners. We call these constraints "tasks", and we investigate the idealised view that such tasks determine model behaviours. We develop a flowchart-like graphical mathematics for tasks that allows us to; (1) offer a unified perspective of approaches in machine learning across domains; (2) design and optimise desired behaviours model-agnostically; and (3) import insights from theoretical computer science into practical machine learning. As a proof-of-concept of the potential practical impact of our theoretical framework, we exhibit and implement a novel "manipulator" task that minimally edits input data to have a desired attribute. Our model-agnostic approach achieves this end-to-end, and without the need for custom architectures, adversarial training, random sampling, or interventions on the data, hence enabling capable, small-scale, and training-stable models.
Diffusion Models for Tabular Data Imputation and Synthetic Data Generation
Venue: arXiv cs.LG 2025
Year: 2025

Data imputation and data generation have important applications for many domains, like healthcare and finance, where incomplete or missing data can hinder accurate analysis and decision-making. Diffusion models have emerged as powerful generative models capable of capturing complex data distributions across various data modalities such as image, audio, and time series data. Recently, they have been also adapted to generate tabular data. In this paper, we propose a diffusion model for tabular data that introduces three key enhancements: (1) a conditioning attention mechanism, (2) an encoder-decoder transformer as the denoising network, and (3) dynamic masking. The conditioning attention mechanism is designed to improve the model's ability to capture the relationship between the condition and synthetic data. The transformer layers help model interactions within the condition (encoder) or synthetic data (decoder), while dynamic masking enables our model to efficiently handle both missing data imputation and synthetic data generation tasks within a unified framework. We conduct a comprehensive evaluation by comparing the performance of diffusion models with transformer conditioning against state-of-the-art techniques, such as Variational Autoencoders, Generative Adversarial Networks and Diffusion Models, on benchmark datasets. Our evaluation focuses on the assessment of the generated samples with respect to three important criteria, namely: (1) Machine Learning efficiency, (2) statistical similarity, and (3) privacy risk mitigation. For the task of data imputation, we consider the efficiency of the generated samples across different levels of missing features.
Optimizing Probabilistic Propagation in Graphs by Adding Edges
Venue: arXiv cs.DS 2025
Year: 2025

Probabilistic graphs are an abstraction that allow us to study randomized propagation in graphs. In a probabilistic graph, each edge is "active" with a certain probability, independent of the other edges. For two vertices $u,v$, a classic quantity of interest, that we refer to as the proximity $\mathcal{P}_{G}(u, v)$, is the probability that there exists a path between $u$ and $v$ all of whose edges are active. For a given subset of vertices $V_s$, the reach of $V_s$ is defined as the minimum over pairs $u \in V_s$ and $v \in V$ of the proximity $\mathcal{P}_{G}(u,v)$. This quantity has been studied in the context of multicast in unreliable communication networks and in social network analysis. We study the problem of improving the reach in a probabilistic graph via edge augmentation. Formally, given a budget $k$ of edge additions and a set of source vertices $V_s$, the goal of Reach Improvement is to maximize the reach of $V_s$ by adding at most $k$ new edges to the graph. The problem was introduced in earlier empirical work in the algorithmic fairness community. We provide the first approximation guarantees and hardness results for Reach Improvement. We prove that the existence of a good augmentation implies a cluster structure for the graph. We use this structural result to analyze a novel algorithm that outputs a $k$-edge augmentation with an objective value that is poly($β^*$), where $β^*$ is the objective value for the optimal augmentation. We also give an algorithm that adds $O(k \log n)$ edges and yields a multiplicative approximation to $β^*$. Our arguments rely on new probabilistic tools for analyzing proximity, inspired by techniques in percolation theory; these tools may be of broader interest. Finally, we show that significantly better approximations are unlikely, under known hardness assumptions related to gap variants of the classic Set Cover problem.
LLM-Select: Feature Selection with Large Language Models
Venue: arXiv cs.LG 2025
Year: 2025

In this paper, we demonstrate a surprising capability of large language models (LLMs): given only input feature names and a description of a prediction task, they are capable of selecting the most predictive features, with performance rivaling the standard tools of data science. Remarkably, these models exhibit this capacity across various query mechanisms. For example, we zero-shot prompt an LLM to output a numerical importance score for a feature (e.g., "blood pressure") in predicting an outcome of interest (e.g., "heart failure"), with no additional context. In particular, we find that the latest models, such as GPT-4, can consistently identify the most predictive features regardless of the query mechanism and across various prompting strategies. We illustrate these findings through extensive experiments on real-world data, where we show that LLM-based feature selection consistently achieves strong performance competitive with data-driven methods such as the LASSO, despite never having looked at the downstream training data. Our findings suggest that LLMs may be useful not only for selecting the best features for training but also for deciding which features to collect in the first place. This could benefit practitioners in domains like healthcare and the social sciences, where collecting high-quality data comes at a high cost.
Understanding the Prevalence of Caste: A Critical Discourse Analysis of Caste-based Marginalization on X
Venue: arXiv cs.HC 2025
Year: 2025

Despite decades of anti-caste efforts, sociocultural practices that marginalize lower-caste groups in India remain prevalent and have even proliferated with the use of social media. This paper examines how groups engaged in caste-based discrimination leverage platform affordances of the social media site X (formerly Twitter) to circulate and reinforce caste ideologies. Using a critical discourse analysis (CDA) approach, we examine the rhetorical and organizing strategies of 50 X profiles representing upper-caste collectives. We find that these profiles leverage platform affordances such as information control, bandwidth, visibility, searchability, and shareability to construct two main arguments: (1) that their upper caste culture deserves a superior status and (2) that they are the "true" victims of oppression in society. These profiles' digitally mediated discursive strategies contribute to the marginalization of lower castes by normalizing caste cultures, strengthening caste networks, reinforcing caste discrimination, and diminishing anti-caste measures. Our analysis builds upon previous HCI conceptualizations of online harms and safety to inform how to address caste-based marginalization. We offer theoretical and methodological suggestions for critical HCI research focused on studying the mechanisms of power along other social categories such as race and gender.
PII-Compass: Guiding LLM training data extraction prompts towards the target PII via grounding
Venue: arXiv cs.CR 2025
Year: 2025

The latest and most impactful advances in large models stem from their increased size. Unfortunately, this translates into an improved memorization capacity, raising data privacy concerns. Specifically, it has been shown that models can output personal identifiable information (PII) contained in their training data. However, reported PIII extraction performance varies widely, and there is no consensus on the optimal methodology to evaluate this risk, resulting in underestimating realistic adversaries. In this work, we empirically demonstrate that it is possible to improve the extractability of PII by over ten-fold by grounding the prefix of the manually constructed extraction prompt with in-domain data. Our approach, PII-Compass, achieves phone number extraction rates of 0.92%, 3.9%, and 6.86% with 1, 128, and 2308 queries, respectively, i.e., the phone number of 1 person in 15 is extractable.
ObfuscaTune: Obfuscated Offsite Fine-tuning and Inference of Proprietary LLMs on Private Datasets
Venue: arXiv cs.CR 2025
Year: 2025

This work addresses the timely yet underexplored problem of performing inference and finetuning of a proprietary LLM owned by a model provider entity on the confidential/private data of another data owner entity, in a way that ensures the confidentiality of both the model and the data. Hereby, the finetuning is conducted offsite, i.e., on the computation infrastructure of a third-party cloud provider. We tackle this problem by proposing ObfuscaTune, a novel, efficient and fully utility-preserving approach that combines a simple yet effective obfuscation technique with an efficient usage of confidential computing (only 5% of the model parameters are placed on TEE). We empirically demonstrate the effectiveness of ObfuscaTune by validating it on GPT-2 models with different sizes on four NLP benchmark datasets. Finally, we compare to a naïve version of our approach to highlight the necessity of using random matrices with low condition numbers in our approach to reduce errors induced by the obfuscation.
Protection Degree and Migration in the Stochastic SIRS Model: A Queueing System Perspective
Venue: arXiv cs.SI 2025
Year: 2025

With the prevalence of COVID-19, the modeling of epidemic propagation and its analyses have played a significant role in controlling epidemics. However, individual behaviors, in particular the self-protection and migration, which have a strong influence on epidemic propagation, were always neglected in previous studies. In this paper, we mainly propose two models from the individual and population perspectives. In the first individual model, we introduce the individual protection degree that effectively suppresses the epidemic level as a stochastic variable to the SIRS model. In the alternative population model, an open Markov queueing network is constructed to investigate the individual number of each epidemic state, and we present an evolving population network via the migration of people. Besides, stochastic methods are applied to analyze both models. In various simulations, the infected probability, the number of individuals in each state and its limited distribution are demonstrated.
Fine-Tuning on Diverse Reasoning Chains Drives Within-Inference CoT Refinement in LLMs
Venue: arXiv cs.CL 2025
Year: 2025

Requiring a large language model (LLM) to generate intermediary reasoning steps, known as Chain of Thought (CoT), has been shown to be an effective way of boosting performance. Previous approaches have focused on generating multiple independent CoTs, combining them through ensembling or other post-hoc strategies to enhance reasoning. In this work, we introduce a novel approach where LLMs are fine-tuned to generate a sequence of Diverse Chains of Thought (DCoT) within a single inference step, which is fundamentally different from prior work that primarily operate on parallel CoT generations. DCoT allows LLMs to gain the ability to perform within-inference refinement of reasoning chains without requiring external feedback. Through a rigorous set of experiments spanning a wide range of tasks that require various reasoning types, we show that fine-tuning on DCoT improves performance over the CoT baseline across model families and scales (1.3B to 70B). These improvements are particularly impactful for tasks with a large result state space, such as those involving numeric answers. Our work is also significant because both quantitative analyses and manual evaluations reveal the observed gains stem from the models' ability to refine an initial reasoning chain by generating a second, improved chain within the same inference step, demonstrating previously elusive self-improvement. Our code and data are publicly available at https://github.com/UKPLab/acl2025-diverse-cot.
A Role of Environmental Complexity on Representation Learning in Deep Reinforcement Learning Agents
Venue: arXiv cs.LG 2025
Year: 2025

We developed a simulated environment to train deep reinforcement learning agents on a shortcut usage navigation task, motivated by the Dual Solutions Paradigm test used for human navigators. We manipulated the frequency with which agents were exposed to a shortcut and a navigation cue, to investigate how these factors influence shortcut usage development. We find that all agents rapidly achieve optimal performance in closed shortcut trials once initial learning starts. However, their navigation speed and shortcut usage when it is open happen faster in agents with higher shortcut exposure. Analysis of the agents' artificial neural networks activity revealed that frequent presentation of a cue initially resulted in better encoding of the cue in the activity of individual nodes, compared to agents who encountered the cue less often. However, stronger cue representations were ultimately formed through the use of the cue in the context of navigation planning, rather than simply through exposure. We found that in all agents, spatial representations develop early in training and subsequently stabilize before navigation strategies fully develop, suggesting that having spatially consistent activations is necessary for basic navigation, but insufficient for advanced strategies. Further, using new analysis techniques, we found that the planned trajectory rather than the agent's immediate location is encoded in the agent's networks. Moreover, the encoding is represented at the population rather than the individual node level. These techniques could have broader applications in studying neural activity across populations of neurons or network nodes beyond individual activity patterns.
Stacked Intelligent Metasurfaces for Wireless Communications: Applications and Challenges
Venue: arXiv cs.IT 2025
Year: 2025

The rapid growth of wireless communications has created a significant demand for high throughput, seamless connectivity, and extremely low latency. To meet these goals, a novel technology -- stacked intelligent metasurfaces (SIMs) -- has been developed to perform signal processing by directly utilizing electromagnetic waves, thus achieving incredibly fast computing speed while reducing hardware requirements. In this article, we provide an overview of SIM technology, including its underlying hardware, benefits, and exciting applications in wireless communications. Specifically, we examine the utilization of SIMs in realizing transmit beamforming and semantic encoding in the wave domain. Additionally, channel estimation in SIM-aided communication systems is discussed. Finally, we highlight potential research opportunities and identify key challenges for deploying SIMs in wireless networks to motivate future research.
reBEN: Refined BigEarthNet Dataset for Remote Sensing Image Analysis
Venue: arXiv cs.CV 2025
Year: 2025

This paper presents refined BigEarthNet (reBEN) that is a large-scale, multi-modal remote sensing dataset constructed to support deep learning (DL) studies for remote sensing image analysis. The reBEN dataset consists of 549,488 pairs of Sentinel-1 and Sentinel-2 image patches. To construct reBEN, we initially consider the Sentinel-1 and Sentinel-2 tiles used to construct the BigEarthNet dataset and then divide them into patches of size 1200 m x 1200 m. We apply atmospheric correction to the Sentinel-2 patches using the latest version of the sen2cor tool, resulting in higher-quality patches compared to those present in BigEarthNet. Each patch is then associated with a pixel-level reference map and scene-level multi-labels. This makes reBEN suitable for pixel- and scene-based learning tasks. The labels are derived from the most recent CORINE Land Cover (CLC) map of 2018 by utilizing the 19-class nomenclature as in BigEarthNet. The use of the most recent CLC map results in overcoming the label noise present in BigEarthNet. Furthermore, we introduce a new geographical-based split assignment algorithm that significantly reduces the spatial correlation among the train, validation, and test sets with respect to those present in BigEarthNet. This increases the reliability of the evaluation of DL models. To minimize the DL model training time, we introduce software tools that convert the reBEN dataset into a DL-optimized data format. In our experiments, we show the potential of reBEN for multi-modal multi-label image classification problems by considering several state-of-the-art DL models. The pre-trained model weights, associated code, and complete dataset are available at https://bigearth.net.
Greedy BST on Permutation Initial Tree
Venue: arXiv cs.DS 2025
Year: 2025

The Greedy binary search tree (BST) algorithm, like the Splay tree, is a prominent candidate for the \emph{dynamic optimality conjecture}. While Greedy satisfies many desirable properties of BST, its cost and analysis to execute a search sequence $S$ is known to depend heavily on the choice of the \emph{initial tree} configuration. Most prior analyses assume a flat (empty) initial tree, under which several tight bounds are established. In this work, we introduce the notion of a \emph{permutation initial tree}, a specific class of non-flat initial tree and prove that for any permutation search sequence $S=(s_1,s_2,\dots, s_n)$, there exists a permutation initial tree $I_p$ such that the cost of Greedy on $I_p$ is same as its cost on the flat initial tree. As an application of our result, we show that the \emph{preorder traversal conjecture} holds for Greedy when the initial tree is a permutation initial tree. While it was previously known that Greedy achieves an $O(n)$ cost on preorder sequences for flat initial tree (Chalermsook et al., FOCS 2015), our result demonstrates that the same linear bound holds when the initial tree is a permutation initial tree. This result also matches the $O(n)$ bound for Splay tree on preorder sequence when the initial tree aligns with the traversal order (Chaudhuri and Höft, SIGACT 1993).
Establishing Provenance Before Coding: Traditional and Next-Gen Software Signing
Venue: arXiv cs.CR 2025
Year: 2025

Software engineers integrate third-party components into their applications. The resulting software supply chain is vulnerable. To reduce the attack surface, we can verify the origin of components (provenance) before adding them. Cryptographic signatures enable this. This article describes traditional signing, its challenges, and the changes introduced by next-generation signing platforms.